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  These notes accompany the Stanford CS class <a href="http://cs231n.stanford.edu/">CS231n: Deep Learning for Computer Vision</a>. For questions/concerns/bug reports, please submit a pull request directly to
  our <a href="https://github.com/cs231n/cs231n.github.io">git repo</a>.
  <br>
  <!-- For questions/concerns/bug reports contact <a href="http://cs.stanford.edu/people/jcjohns/">Justin Johnson</a> regarding the assignments, or contact <a href="http://cs.stanford.edu/people/karpathy/">Andrej Karpathy</a> regarding the course notes. You can also submit a pull request directly to our <a href="https://github.com/cs231n/cs231n.github.io">git repo</a>. -->
  <!-- <br> -->
  <!-- We encourage the use of the <a href="https://hypothes.is/">hypothes.is</a> extension to annote comments and discuss these notes inline. -->
</div>

<div class="home">
  <div class="materials-wrap">
    <div class="module-header">Spring 2024 Assignments</div>
    <div class="materials-item">
      <a href="assignments2024/assignment1/">Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected Neural Network</a>
    </div>
    <div class="materials-item">
      <a href="assignments2024/assignment2/">Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Pytorch & Network Visualization</a>
    </div>
    <div class="materials-item">
      <a href="assignments2024/assignment3/">Assignment #3: Network Visualization, Image Captioning with RNNs and Transformers, Generative Adversarial Networks, Self-Supervised Contrastive Learning</a>
    </div>
  </div>

  <!-- <div class="materials-wrap">
    <div class="module-header">Spring 2021 Assignments</div>
      <div class="materials-item">
        <a href="assignments2021/assignment1/">Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected Neural Network</a>
      </div>
      <div class="materials-item">
        <a href="assignments2021/assignment2/">Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Frameworks</a>
      </div>
      <div class="materials-item">
        <a href="assignments2021/assignment3/">Assignment #3: Image Captioning with RNNs and Transformers, Network Visualization,
          Generative Adversarial Networks, Self-Supervised Contrastive Learning</a>
      </div>
  </div> -->
  <!--
    <div class="materials-item">
      <a href="assignments2019/assignment2/">
        Assignment #2: Fully Connected Nets, Batch Normalization, Dropout,
        Convolutional Nets
      </a>
    </div>

    <div class="materials-item">
      <a href="assignments2019/assignment3/">
        Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning
  with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks
      </a>
    </div> -->
  <!--
    <div class="module-header">Spring 2018 Assignments</div>

    <div class="materials-item">
      <a href="assignments2018/assignment1/">
        Assignment #1: Image Classification, kNN, SVM, Softmax, Neural Network
      </a>
    </div>

    <div class="materials-item">
      <a href="assignments2018/assignment2/">
        Assignment #2: Fully-Connected Nets, Batch Normalization, Dropout,
        Convolutional Nets
      </a>
    </div>

    <div class="materials-item">
      <a href="assignments2018/assignment3/">
        Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning
        with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks
      </a>
    </div>
    -->

  <!--
    <div class="module-header">Winter 2016 Assignments</div>

    <div class="materials-item">
      <a href="assignments2016/assignment1/">
        Assignment #1: Image Classification, kNN, SVM, Softmax, Neural Network
      </a>
    </div>

    <div class="materials-item">
      <a href="assignments2016/assignment2/">
        Assignment #2: Fully-Connected Nets, Batch Normalization, Dropout,
        Convolutional Nets
      </a>
    </div>

    <div class="materials-item">
      <a href="assignments2016/assignment3/">
        Assignment #3: Recurrent Neural Networks, Image Captioning,
        Image Gradients, DeepDream
      </a>
    </div>
    -->

  <!--
    <div class="module-header">Winter 2015 Assignments</div>

    <div class="materials-item">
      <a href="assignment1/">
        Assignment #1: Image Classification, kNN, SVM, Softmax
      </a>
    </div>

    <div class="materials-item">
      <a href="assignment2/">
        Assignment #2: Neural Networks, ConvNets I
      </a>
    </div>

    <div class="materials-item">
      <a href="assignment3/">
        Assignment #3: ConvNets II, Transfer Learning, Visualization
      </a>
    </div>
  -->

  <div class="module-header">Module 0: Preparation</div>

  <div class="materials-item">
    <a href="setup-instructions/">
      Software Setup
    </a>
  </div>

  <div class="materials-item">
    <a href="python-numpy-tutorial/">
      Python / Numpy Tutorial (with Jupyter and Colab)
    </a>
  </div>
  <!--
    <div class="materials-item">
      <a href="terminal-tutorial/">
        Terminal.com Tutorial
      </a>
    </div>
-->
  <!-- <div class="materials-item">
      <a href="https://github.com/cs231n/gcloud">
        Google Cloud Tutorial
      </a>
    </div> -->
  <!-- <div class="materials-item">
      <a href="aws-tutorial/">
        AWS Tutorial
      </a>
    </div> -->

  <!-- hardcoding items here to force a specific order -->
  <div class="module-header">Module 1: Neural Networks</div>

  <div class="materials-item">
    <a href="classification/">
      Image Classification: Data-driven Approach, k-Nearest Neighbor, train/val/test splits
    </a>
    <div class="kw">
      L1/L2 distances, hyperparameter search, cross-validation
    </div>
  </div>

  <div class="materials-item">
    <a href="linear-classify/">
      Linear classification: Support Vector Machine, Softmax
    </a>
    <div class="kw">
      parameteric approach, bias trick, hinge loss, cross-entropy loss, L2 regularization, web demo
    </div>
  </div>

  <div class="materials-item">
    <a href="optimization-1/">
      Optimization: Stochastic Gradient Descent
    </a>
    <div class="kw">
      optimization landscapes, local search, learning rate, analytic/numerical gradient
    </div>
  </div>

  <div class="materials-item">
    <a href="optimization-2/">
      Backpropagation, Intuitions
    </a>
    <div class="kw">
      chain rule interpretation, real-valued circuits, patterns in gradient flow
    </div>
  </div>

  <div class="materials-item">
    <a href="neural-networks-1/">
      Neural Networks Part 1: Setting up the Architecture
    </a>
    <div class="kw">
      model of a biological neuron, activation functions, neural net architecture, representational power
    </div>
  </div>

  <div class="materials-item">
    <a href="neural-networks-2/">
      Neural Networks Part 2: Setting up the Data and the Loss
    </a>
    <div class="kw">
      preprocessing, weight initialization, batch normalization, regularization (L2/dropout), loss functions
    </div>
  </div>

  <div class="materials-item">
    <a href="neural-networks-3/">
      Neural Networks Part 3: Learning and Evaluation
    </a>
    <div class="kw">
      gradient checks, sanity checks, babysitting the learning process, momentum (+nesterov), second-order methods,
      Adagrad/RMSprop, hyperparameter optimization, model ensembles
    </div>
  </div>

  <div class="materials-item">
    <a href="neural-networks-case-study/">
      Putting it together: Minimal Neural Network Case Study
    </a>
    <div class="kw">
      minimal 2D toy data example
    </div>
  </div>

  <div class="module-header">Module 2: Convolutional Neural Networks</div>

  <div class="materials-item">
    <a href="convolutional-networks/">
      Convolutional Neural Networks: Architectures, Convolution / Pooling Layers
    </a>
    <div class="kw">
      layers, spatial arrangement, layer patterns, layer sizing patterns, AlexNet/ZFNet/VGGNet case studies,
      computational considerations
    </div>
  </div>

  <div class="materials-item">
    <a href="understanding-cnn/">
      Understanding and Visualizing Convolutional Neural Networks
    </a>
    <div class="kw">
      tSNE embeddings, deconvnets, data gradients, fooling ConvNets, human comparisons
    </div>
  </div>

  <div class="materials-item">
    <a href="transfer-learning/">
      Transfer Learning and Fine-tuning Convolutional Neural Networks
    </a>
  </div>

  <div class="module-header">Student-Contributed Posts</div>

  <div class="materials-item">
    <a href="choose-project/">
      Taking a Course Project to Publication
    </a>
    <a href="rnn/">
      Recurrent Neural Networks
    </a>
  </div>

</div>
</div>
